• DocumentCode
    1707928
  • Title

    Discovering multivariate linear relationship securely

  • Author

    Wu, Ningning ; Zhang, Jing ; Ning, Li

  • Author_Institution
    Dept. of Inf. Sci., Arkansas Univ., Little Rock, AR, USA
  • fYear
    2005
  • Firstpage
    436
  • Lastpage
    437
  • Abstract
    This paper considers the privacy-preserving cooperative linear system of equations (PPC-LSE) problem in a large, heterogeneous, distributed database scenario. It proposes a privacy-preserving algorithm to discover multivariate linear relationship based on factor analysis. Compared with other PPC-LSE algorithms, the proposed algorithm not only significantly reduces the communication cost, but also avoids the random matrix generation of either party to hide private information.
  • Keywords
    data mining; data privacy; distributed databases; security of data; very large databases; PPC-LSE; data mining; data security; distributed database; factor analysis; heterogeneous database; large database; multivariate linear relationship discovery; privacy-preserving cooperative linear system of equations; private information hiding; Algorithm design and analysis; Data mining; Diseases; Distributed databases; Equations; Information analysis; Linear systems; Partitioning algorithms; Protection; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Assurance Workshop, 2005. IAW '05. Proceedings from the Sixth Annual IEEE SMC
  • Print_ISBN
    0-7803-9290-6
  • Type

    conf

  • DOI
    10.1109/IAW.2005.1495989
  • Filename
    1495989
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1707928